Credit analytics is a way to arrive at creditworthiness of a business or an organization i.e. the ability of the Business or organization to meet its financial obligations. In technical terms, the crux of credit risk analysis lies in identifying the appropriate level of default risk associated with investment in a particular entity.
Peril of credit risk:
The credit risk associated with repayment of loan is one of the highly significant risks that commercial banks are plagued with. It turns out to be even more significant as almost 40% of total revenue of commercial banks is generated from the bank’s credit related assets. Therefore the importance of a credit analytics lies in building statistical models on available past data to forecast key parameters based on which business decisions can be taken. A significant amount of time is spent on collection of data. This data is used to refine statistical model to predict outcomes of various business decisions under different scenarios. The models can also be used to identify businesses which may show a change in debt rating and hence find the probable change in profits.
The most pursued businesses among lenders and use of analytics:
- Credit analytics based on robust risk models can help lenders to make better lending decisions in low-cost ways. It can also help institutions to identify unbanked and unexplored areas which can be better served.
- Traditional consumer finance where the lenders charge higher interest rate along with a penalty fee for higher default incidence. The optimal interest rate can be identified using analytics by ensuring right balance between risk and return.
- Potential good customers like female owned small businesses (micro-credit) which focuses on fostering strong relationships with short-term loans as seen in many towns and villages of India can be identified using analytics to grow business.
The conservative methods of credit analysis:
- Identifying and reducing fraud.
- Ability of an entity to repay based on revenue and current debt.
- Willingness to repay based on past credit information turns out to be ineffective in the low income groups.
As the low income customers mostly have no access to formal financial institutions and services, there is no record of their borrowing behavior. Debt capacity is not easy to arrive at as these customers often receive income in cash which is not a means to use as collateral for debt. Currently non-traditional lenders are able to address these hiccups and lend money to these customers. However they offer huge potential to the formal money lending institutions to bank on. Hence new research on these lines is being done by credit analytics team all over world. Converting data into credit insights through advanced credit-risk modeling computing capabilities will help in capturing the predictive potential from the data available.